The past decade has witnessed the rapid development of ML and DL methodologies in agricultural systems, showcased by great successes in variety of agricultural applications. However, these conventional ML/DL models have certain limitations: They heavily rely on large, costly-to-acquire labeled datasets for training, require specialized expertise for development and maintenance, and are mostly tailored for specific tasks, thus lacking generalizability. Recently, foundation models have demonstrated remarkable successes in language and vision tasks across various domains. These models are trained on a vast amount of data from multiple domains and modalities. Once trained, they can accomplish versatile tasks with just minor fine-tuning and minimal task-specific labeled data. Despite their proven effectiveness and huge potential, there has been little exploration of applying FMs to agriculture fields. Therefore, this study aims to explore the potential of FMs in the field of smart agriculture. In particular, we present conceptual tools and technical background to facilitate the understanding of the problem space and uncover new research directions in this field. To this end, we first review recent FMs in the general computer science domain and categorize them into four categories: language FMs, vision FMs, multimodal FMs, and reinforcement learning FMs. Subsequently, we outline the process of developing agriculture FMs and discuss their potential applications in smart agriculture. We also discuss the unique challenges associated with developing AFMs, including model training, validation, and deployment. Through this study, we contribute to the advancement of AI in agriculture by introducing AFMs as a promising paradigm that can significantly mitigate the reliance on extensive labeled datasets and enhance the efficiency, effectiveness, and generalization of agricultural AI systems.

翻译：过去十年，机器学习与深度学习方法在农业系统中快速发展，在各类农业应用中取得了巨大成功。然而，这些传统机器学习/深度学习模型存在一定局限性：它们严重依赖规模庞大且获取成本高昂的标注数据集进行训练，需要专业化的开发与维护知识，且大多针对特定任务定制，缺乏泛化能力。近年来，基础模型在跨领域的语言与视觉任务中展现出显著成功。这些模型使用来自多个领域和模态的海量数据进行训练。一旦训练完成，它们只需少量微调和极少的任务特定标注数据即可完成多种任务。尽管基础模型已证明其有效性和巨大潜力，但将其应用于农业领域的研究仍十分有限。因此，本研究旨在探索基础模型在智慧农业领域的潜力。具体而言，我们提出了概念性工具和技术背景，以帮助理解问题空间并发现该领域的新研究方向。为此，我们首先回顾了通用计算机科学领域中的最新基础模型，并将其分为四类：语言基础模型、视觉基础模型、多模态基础模型和强化学习基础模型。随后，我们概述了农业基础模型的开发过程，并讨论了它们在智慧农业中的潜在应用。我们还探讨了开发农业基础模型所面临的独特挑战，包括模型训练、验证和部署。通过本研究，我们引入了农业基础模型作为一种有前景的范式，可显著减轻对大规模标注数据集的依赖，并提升农业人工智能系统的效率、有效性和泛化能力，从而推动人工智能在农业领域的发展。